lbr_fri_ros2_stack

ROS 2 packages for the KUKA LBR, including communication to the real robot via the Fast Robot Interface (FRI) and Gazebo simulation support. Included are the iiwa7, iiwa14, med7, and med14.

LBR IIWA 7 R800	LBR IIWA 14 R820	LBR Med 7 R800	LBR Med 14 R820

Documentation

Full documentation available on Read the Docs.

Quick Start

1. Install ROS 2 development tools

   sudo apt install ros-dev-tools
   

2. Create a workspace, clone, and install dependencies

   source /opt/ros/humble/setup.bash
   export FRI_CLIENT_VERSION=1.15
   mkdir -p lbr-stack/src && cd lbr-stack
   vcs import src --input https://raw.githubusercontent.com/lbr-stack/lbr_fri_ros2_stack/humble/lbr_fri_ros2_stack/repos-fri-${FRI_CLIENT_VERSION}.yaml
   rosdep install --from-paths src -i -r -y
   

[!NOTE] FRI client is cloned from fri and must be available as branch, refer README.

3. Build

   colcon build --symlink-install
   

4. Launch the simulation via

   source install/setup.bash
   ros2 launch lbr_bringup bringup.launch.py \
       model:=iiwa7 # [iiwa7, iiwa14, med7, med14] \
       sim:=true # [true, false] \
       rviz:=true # [true, false] \
       moveit:=true # [true, false]
   

[!TIP] List all arguments for the launch file via ros2 launch lbr_bringup bringup.launch.py -s

Now, run the demos. To get started with the real robot, checkout the Hardware Setup.

Citation

If you enjoyed using this repository for your work, we would really appreciate ❤️ if you could leave a ⭐ and / or cite it, as it helps us to continue offering support.

@misc{huber2023lbrstack,
      title={LBR-Stack: ROS 2 and Python Integration of KUKA FRI for Med and IIWA Robots}, 
      author={Martin Huber and Christopher E. Mower and Sebastien Ourselin and Tom Vercauteren and Christos Bergeles},
      year={2023},
      eprint={2311.12709},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

Acknowledgements

Open Source Contributors

We would like to acknowledge all contributors 🚀

lbr_fri_ros2_stack

fri

Organizations and Grants

We would further like to acknowledge following supporters:

Logo	Notes
	This work was supported by core and project funding from the Wellcome/EPSRC [WT203148/Z/16/Z; NS/A000049/1; WT101957; NS/A000027/1].
	This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016985 (FAROS project).
	Built at RViMLab.
	Built at CAI4CAI.
	Built at King's College London.